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covariance analysis by using R

2 messages · 孟欣, Wuming Gong

#
Hello sir:
Here's a question on covariance analysis which needs your help.
There're 3 experiments,and x refers to control while y refers to experimental result.
The purpose is to compare the "y" values across the 3 experiments. 

experiment_1:
x:0.1 0.2 0.3 0.4 0.5
y:0.5 0.6 0.6 0.7 0.9

experiment_2:
x:1 2 3   4   5
y:3 4 6.5 7.5 11

experiment_3:
x:10 20 30 40 50
y:18 35 75 90 98

Apparently,the control("x") isn't at the similar level so that we can't compare the "y" directly through ANOVA.
We must normalize "y" via "x" in order to eliminate the influence of  different level of "x".
The method of normalize I can get is "covariance analysis",since "x" is the covariant of y.

My question is:
How to perform "covariance analysis" by using R?
After this normalization,we can get the according "normalized y" of every "original y".

All in all,the "normalized y" of every "original y" is what I want indeed.


Thanks a lot!

My best regards!






------------------------------
*******************************************
Xin Meng 
Capitalbio Corporation
National Engineering Research Center 
for Beijing Biochip Technology 
Microarray and Bioinformatics Dept. 
Research Engineer
Tel: +86-10-80715888/80726868-6364/6333 
Fax: +86-10-80726790
Email£ºxmeng at capitalbio.com 
Address:18 Life Science Parkway, 
Changping District, Beijing 102206, China
2 days later
#
You may fit the model using lm() directly - R will set up a coding for
qualitative predictor automatically (taking experiments as qualitative
predictor).

HTH

Wuming
On 5/18/05, ©sªY <xmeng at capitalbio.com> wrote: